The present disclosure relates to automatic license plate recognition. More specifically, the present disclosure relates to improved license plate recognition using an adaptive, extended feature set.
Automatic License Plate Recognition (ALPR) systems are being used more by various companies and organizations as means for identifying and recognizing motor vehicles. For example, many toll highways now use an automatic tolling system that includes license plate recognition. Many tolling systems use a small transponder mounted somewhere on a vehicle. License plate recognition is commonly used to verify that the vehicle using the transponder is the vehicle to which the transponder has been issued. Similar systems are used for parking structures.
Law enforcement entities also use ALPR systems. Red light cameras, speeding cameras, automatic identification of stolen vehicles, and other applications have been developed to assist law enforcement departments. ALPR systems are typically used to identify the owner of a violating vehicle (e.g., in a red light camera or speeding trap camera) by capturing an image of the license plate and identifying the owner of the vehicle based upon a comparison of the license plate content (e.g., the issuing state and the license plate number/letter combination) against one or more databases of vehicle registration information. Similarly, an ALPR system can be used to identify a stolen vehicle by comparing the license plate content of a vehicle against a database of stolen vehicle license plates to determine any potential matches.
Regardless of the application, ALPR systems have inherent drawbacks. Due to naturally occurring factors such as weather and time of day, an image of a license plate may be blurry or incomplete. Similarly, a trailer hitch, license plate cover, or other similar added components may also block or obscure an image of a license plate. Additional factors such as issuing state of license plate not recognized, camera out of calibration, damage to the license plate, contamination such as dirt or other debris on the license plate, and other such factors may also contribute to an obscure license plate image. When a license plate is obscured or otherwise cannot be automatically confirmed, human intervention is required to verify the license plate number.
FIG. 1 illustrates a process used by a conventional ALPR system. The process as shown includes an offline training or initialization process for the ALPR system. The initialization process includes the ALPR system reading 102 a test image including a license plate number. Reading 102 the test image may include applying an object character recognition (OCR) identification algorithm to the test image. An exemplary OCR identification algorithm isolates one or more characters from the license plate number of the test image, segments multiple characters into a single character image, and compares the single character image against a set of standard character images to determine each character that is read from the test image. After determining each character in the test image, the OCR identification algorithm produces a results set including a character string indicating the characters contained in the license plate of the test image. The results of reading 102 are corrected by a human operator. For example, if a test license plate image reads “New York ABC-123,” the OCR identification algorithm used by the ALPR system may interpret the test license plate image to read 102 “Pennsylvania A3C-123.” The human operator may manually examine the test image and enter the correct information contained in the image. Based upon the entered information, the OCR parameters for the OCR identification algorithm may be adjusted 104 to increase the accuracy of the OCR identification algorithm. For example, based upon the entered information, the parameters for identifying the issuing state may be adjusted 104 to include additional features for use in recognizing a license plate issued by the state government of New York. Once the ALPR system reaches a certain accuracy level (e.g., 80% accurate for a set of 100 test images), the ALPR system or an operator of the ALPR system may determine 106 that the training is complete. Once the ALPR system training is complete, the ALPR system enters an active state. If the training is not determined 106 to be complete, additional test images may be read 102, and the OCR parameters may be further adjusted 104 as previously discussed. It should be noted that the training or initialization procedure may be completed once for each ALPR system upon installation, intermittently or at regular time intervals, such as daily, weekly, monthly, etc., to ensure overall system accuracy and performance is maintained.
After training, the ALPR system may operate in a ready state until a license plate image is obtained from a vehicle passing an image capture device of an ALPR system. The image capture device may include a digital still camera, a digital video recording camera, a photocell, or other devices capable of capturing and producing an image. The image capture device may be triggered by a vehicle detecting sensor such as a weight sensor embedded in the road or a photosensitive eye directed toward a traffic lane. The vehicle sensing device may be configured such that when a vehicle is passing the vehicle sensing device the image capture device is triggered. The image capture device then captures at least one image of the vehicle. In some applications, the ALPR system may include multiple image capture devices configured to photograph multiple parts of a vehicle, such as front bumper, rear bumper, windshield, and various other parts of the vehicle. These images may then be combined into a single representative vehicle image or a set of representative vehicle images. The image or images may be read 108 by the ALPR system.
The ALPR system performs 110 the OCR identification algorithm with the set parameters from the training or initialization process. Depending on the quality of the vehicle image, the OCR identification algorithm may be able to identify the characters representing the vehicle's license plate number and record those characters in a character string. If a resulting character string is determined 112 by the OCR identification algorithm, the results are reported 116 to a particular business or organization such as a billing authority for a highway department. If a resulting character string is not determined 112 by the OCR identification algorithm, the vehicle image or images are routed to a human operator such that human interpretation of the license plate from the images is obtained 114. The results of the human interpretation are then reported 116.
The above process is used in many applications, including automated toll booths, speeding cameras, red light cameras, and other similar applications. However, each application has inherent drawbacks. For example, weather conditions may impact the quality of the image obtained. If it is raining or foggy when an image is obtained, the OCR identification algorithm may not produce a result and thus human interpretation is required to identify the image. Similarly, images obtained at night may also lack clarity and require a higher level of human interpretation. Reducing human interpretation even 1% in a high traffic area may lead to considerable financial savings.